# RandomForestRegressor in Julia

**URL:** <https://discourse.julialang.org/t/randomforestregressor-in-julia/84425>\
**Category:** Machine Learning\
**Created:** [July 18, 2022, 8:26pm UTC](https://discourse.julialang.org/t/randomforestregressor-in-julia/84425 "2022-07-18T20:26:01Z")\
**Posts on this page:** 1\
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**Author:** ![Palli](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/palli/32/3380_2.png) [@Palli](https://discourse.julialang.org/u/Palli)\
**Post date:** [July 18, 2022, 9:17pm UTC](https://discourse.julialang.org/t/randomforestregressor-in-julia/84425/3 "2022-07-18T21:17:49Z")

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Hi, you reminded me, I just saw (not yet registered, is in the General queue; can still be used):

> **[GitHub - rikhuijzer/StableTrees.jl: This package is DEPRECATED. Use SIRUS.jl...](https://github.com/rikhuijzer/StableTrees.jl)**
>
> This package is DEPRECATED. Use SIRUS.jl instead. Contribute to rikhuijzer/StableTrees.jl development by creating an account on GitHub.

> This package implements the **S** table and **I** nterpretable **RU** le **S** ets (SIRUS) for classification. Regression is also technically possible but not yet implemented.
> 
> The SIRUS algorithm was presented by Bénard et al. in 2020 and 2021. In short, SIRUS combines the predictive accuracy of random forests with the explainability of decision trees while remaining stable. […]

Intriguing, but not too helpful since you’re looking for regression (you can look up if available already through e.g. Python/SciKitLearn?).

I just googled a bit, and you can see how RandomForestRegressor is used here: [https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Decision\_Tree\_Regression\_Julia.ipynb](https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Decision_Tree_Regression_Julia.ipynb)

and RandomizedSearchCV here (though with a classifier, I’m unfamiliar with this but since also works with regression according to SciKitLearn’s docs, should also be workable from Julia): [https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Randomized\_Search.ipynb](https://github.com/cstjean/ScikitLearn.jl/blob/master/examples/Randomized_Search.ipynb)

I’m more curious myself if possible, and how used without SciKitlearn (while very viable to use with Python code):

> [@Comparison between Julia and Python Random Forest Regression](https://discourse.julialang.org/t/comparison-between-julia-and-python-random-forest-regression/28033):
>
> Hi, I am doing a comparison between Julia and Python by converting a python ML code into Julia and the documenting the time taken by both the codes for different sections. I am using Random Forest algorithm. In Julia, I am using DecisionTree.jl package to do the same. In the documentation it says Python-based ScikitLearn is used and yet my results are showing that for training the Random Forest model, Julia is 150% faster than Python. Can someone guide me as to why this is so since, DecisionTr…

I did find this code (for pre-1.0 Julia):

> **[GitHub - bicycle1885/RandomForests.jl: Random Forests in Julia](https://github.com/bicycle1885/RandomForests.jl)**
>
> Random Forests in Julia. Contribute to bicycle1885/RandomForests.jl development by creating an account on GitHub.

and this for current:

> **[GitHub - bpr/CART.jl: Clasification And Regression Trees](https://github.com/bpr/CART.jl)**
>
> Clasification And Regression Trees. Contribute to bpr/CART.jl development by creating an account on GitHub.

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